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Area of Science:

Control Systems Engineering
Nonlinear Dynamics
Artificial Intelligence

Background:

High-order nonlinear systems present significant control challenges due to their complex dynamics.
Full-state constraints in these systems complicate controller design and stability analysis.
Existing control methods often struggle with computational complexity and limited constraint adaptability.

Purpose of the Study:

To develop an adaptive prescribed-time neural controller for high-order nonlinear systems with full-state constraints.
To address the "explosion of complexity" inherent in traditional backstepping methods.
To enhance the adaptability of control strategies to various state constraint types.

Main Methods:

Design of a prescribed-time bounded stability criterion.
Construction of an adaptive prescribed-time filter to manage filter error stability.
Development of a transformation approach for broader state constraint accommodation.
Utilizing radial basis function neural networks (RBFNNs) to approximate unknown nonlinear functions.

Main Results:

The proposed adaptive prescribed-time neural control scheme guarantees prescribed-time stability for the closed-loop system.
All system states are proven to remain within their defined constraints.
Comparative simulations demonstrate the effectiveness and superiority of the developed control strategy.

Conclusions:

The developed adaptive prescribed-time neural controller effectively manages high-order nonlinear systems with full-state constraints.
The approach offers improved stability guarantees and broader applicability compared to existing methods.
This work provides a robust solution for complex control problems in prescribed-time frameworks.